1. Technical Field
The present invention relates to Combined Cooling, Heating and Power (CCHP) systems, and more particularly, to the optimization of a CCHP system for energy and cost savings.
2. Discussion of the Related Art
CCHP systems integrate cooling, heating and power generation capabilities on one site. A key feature of this technology is that waste heat is recovered and utilized to satisfy thermal demands such as space heating, cooling and hot water needs in a facility. A CCHP system can improve overall energy efficiency so that facility operation cost can be reduced. A CCHP system can potentially reduce emissions (e.g., since less fuel is burned to meet the same demand) and enhance energy reliability (e.g., by way of distributed, on-site generation). These features have made CCHP systems a popular energy efficient solution to meet thermal and electricity demands.
To realize the full potential of cost reduction for CCHP systems, carefully designed control systems are needed. CCHP systems are comprised of various components. The dynamics of these components can be very different, and may have different time scales. A Real-Time-Optimization (RTO)/supervisory control framework is usually employed to control such systems. Decision making in RTO involves two layers: on the higher level, set-points for all components are determined by solving an optimization problem that aims to minimize some economic cost function; on the lower level, the control problems are handled apart from the optimization, on a faster scale: feedback controllers ensure that all components track their set-points.
Due to the size and complexity of CCHP systems, the optimization problem can be very large and highly nonlinear. It is challenging to solve such problems in real-time. Integer variables add more difficulty to the already complex problem. In a CCHP system, the integer variables could come from the on/off states of components, charging/discharging status for thermal energy storage (TES), or any component that operates in a discrete manner. For such a large mixed integer nonlinear program (MINLP) a straightforward approach is used to solve the optimization directly using commercial solvers. However, this is not efficient as it fails to address the structure of the particular problem. It can be time-consuming for the solution to converge to a desired accuracy, thus making it difficult to meet the real-time requirement.